Estimating article publication dates and authors based on social media context

ABSTRACT

Probable origination date may be derived by using a stream of data captured, for example, from the Internet and from other documentation sources such as historical information about a target object, its author, related environmental data, social media data, blogs, microblogs, posts, historical information, and/or other data sources. Techniques such as textual analysis, statistical analytics, and/or artificial intelligence may combine and correlate the information from data sources to extract clues that may indicate the original author and date of authorship. Based on the number of conflicting or validating references, and the relationships between them, a probability or confidence score in the accuracy of the analysis may be generated.

FIELD

The present application relates generally to computers and computerapplications, and more particularly to estimating article publicationdates based on computer-implemented social media context.

BACKGROUND

A user researching on a specific subject may ordinarily find manyarticles and references with conflicting or differing information.Often, it is important to determine the version number or publicationdate of an article in order to understand the value of the material andfor example to know whether it is current or obsolete. Discrepancies insimilar reference materials can be caused by outdated documentation oroutdated versions that have been replaced or refuted by newer findings,for example, in the areas of scientific discoveries, medical studies,textbooks, or versions of programs and software packages.

It is not always easy or even possible to determine the origination dateof a piece of information if it has been posted and re-posted numeroustimes, for example, by people in social networks, by news commentatorsor historical information sources. This issue may be especiallyprevalent in academic papers and many technology blogs.

While cached Internet search results may produce a date, a timestamp ofa cached web document does not necessarily correlate to when thedocument was created. For example, the timestamp might have been updatedlong after the initial publication, or a user might have posted an olddocument on a new website which generated an artificially new cachetimestamp.

BRIEF SUMMARY

A method and system to estimate initial publication information based onsocial media context analysis may be provided. The method, in oneaspect, may comprise receiving via a user interface, a search stringassociated with an object. The method may also comprise searching forone or more documents having a title that matches the search string andpublication information associated with the one or more documents, thepublication information comprising at least one or more authors andpublication dates. The method may also comprise searching for the one ormore documents that contain the search string within the content of theone or more documents and the publication information associated withthe one or more documents using a natural language processing technique,responsive to not finding the one or more documents having the titlethat matches the search string. The method may also comprise searchingsocial media data for information associated with the search string,information associated with a topic associated with the search string,and information associated with the one or more documents and thepublication information. The method may also comprise resolving the oneor more documents and the publication information associated with theone or more documents and the information found in the social media datausing an artificial intelligence technique. The method may also comprisegenerating a report comprising the publication information. The methodmay also comprise presenting the report on the user interface.

In one aspect, the report may comprise an assigned probabilityassociated with each of the one or more authors and publication dates.The publication dates may be listed as ranges of dates in the report.The resolving the one or more documents and the publication informationassociated with the one or more documents and the information found inthe social media data may further comprise generating a timeline usingthe one or more documents and the publication information associatedwith the one or more documents and the information found in the socialmedia data.

A system for estimating initial publication information based on socialmedia context analysis, in one aspect, may comprise a user interfaceoperable to run on one or more processors, the user interface operableto receive a search string associated with an object. A historicalanalytics engine may be operable to run on the one or more processors,the historical engine further operable to search for one or moredocuments having a title that matches the search string and publicationinformation associated with the one or more documents. The publicationinformation may comprise at least one or more authors and publicationdates. The historical analytics engine may be further operable to searchfor the one or more documents that contain the search string within thecontent of the one or more documents and the publication informationassociated with the one or more documents using a natural languageprocessing technique. A stream analytics engine may be operable to runon the one or more processors, the stream analytics engine furtheroperable to search social media data for information associated with thesearch string, topic associated with the search string, and the one ormore documents and the publication information. A media analytics enginemay be operable to run on the one or more processors, the mediaanalytics engine further operable to resolve the one or more documentsand the publication information associated with the one or moredocuments and the information found in the social media data using anartificial intelligence technique. The media analytics engine may befurther operable to generate a report comprising the publicationinformation and present the report on the user interface.

A computer readable storage medium storing a program of instructionsexecutable by a machine to perform one or more methods described hereinalso may be provided.

Further features as well as the structure and operation of variousembodiments are described in detail below with reference to theaccompanying drawings. In the drawings, like reference numbers indicateidentical or functionally similar elements.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating components of a system in oneembodiment that identifies publication dates for objects such asdocuments.

FIG. 2 is a flow diagram illustrating an overview of an analytics methodfor determining or estimating article or object publication dates in oneembodiment of the present disclosure.

FIG. 3 is another diagram illustrating components that may estimateinitial publication information of an object (e.g., article or document)based on social media context in one embodiment of the presentdisclosure.

FIG. 4 illustrates a schematic of an example computer or processingsystem that may implement the publication date estimation system in oneembodiment of the present disclosure.

DETAILED DESCRIPTION

Techniques are presented for determining publication dates for articlesor documents, for example, returned in a search result by a searchengine. Techniques of the present disclosure may be implemented as acomputer-implemented process or method, and/or a computer system and mayimprove and/or enhance computer-oriented technology, for example, searchengine technology. The techniques of the present disclosure in oneembodiment allow a user searching for specific information to request atemporal report as to the probable origination date of any or allreferences returned by a search engine and the confidence level in theaccuracy of the timing information. The report may be optionallydisplayed along with the search results.

In one embodiment, the derivation of the probable origination date maybe accomplished using a stream of data (also referred to as Big Data)captured from the Internet and from other documentation sources such ashistorical information about the target article (or document), itsauthor, related environmental data, social media data, blogs,microblogs, posts, historical information, and/or other data sources. Inone embodiment, the techniques of the present disclosure may includeusing textual analysis, statistical analytics, and/or artificialintelligence, for example, to combine and correlate all of thisinformation (from data sources) to extract clues that would indicate whothe original author (or version) might be and when he/she may havecreated the article or version. Based on the number of conflicting orvalidating references, and the relationships between them, a methodand/or system of the present disclosure may generate a probability orconfidence score in the accuracy of the analysis.

Consider the following example scenario. A search on a topic or subjectmatter, for example, Aborigine Tribal Practices returns one blog byauthor M that contains several paragraphs on tribal customs inAustralia. A social network post by author N is found that contains onephrase identical to a phrase in author M's blog. One might ask whereauthor M obtained the information because author M is a county clerk ofa county in state I and author N is a mystery novelist. Neither lookslike a good match to the subject matter. For this example, assume theseare the only two references to this material on the Internet. Author M'sblog was written Feb. 12, 2007. Author N's post is dated May 6, 2008.Author M's blog was written earlier than Author N's post. To the casualuser, it may appear that Author M is the originator of the material andthat Feb. 12, 2007 is the date it was written. But since neither is theobvious originator of the material, a method and/or system of thepresent disclosure in one embodiment may try to find more informationabout Author M and Author N using social media and other sources.

In one embodiment, a method and/or system of the present disclosure mayutilize technology that captures and analyzes data in motion. An exampleof such technology includes IBM® InfoSphere® Streams (from InternationalBusiness Machines Corporation, Armonk, New York), an analytic platformor tool that allows user-developed applications to ingest, analyze andcorrelate information as it arrives from real-time sources. In oneembodiment, for data at rest, a method and/or system of the presentdisclosure may utilize technology providing a programming framework thatallows for the distributed processing of large data sets acrosscomputers, to analyze historical data. An example of such a frameworkmay include Hadoop® (from the Apache Software Foundation, Forest Hill,Maryland).

A technique in the present disclosure utilizes data analytics ofhistorical and social media data to estimate the probable originalauthor and original publication date of the target object of a usersearch on a topic, article, document, news story, commentary, or otherobject such that the user can evaluate the probability that thereferences returned in the search are useful, current, relevant andauthentic based on the author and date or not verifiable and/orobsolete.

FIG. 1 is a diagram illustrating components of a system in oneembodiment that identifies publication dates and/or authors for objectssuch as documents. A user interface 102 allows a user to enter a searchstring associated with an object or document, for example, a documenttitle, a specific document version number, a reference within acommunication such as a Web or Internet post, a video stream or anyother media containing natural language topics, phrases, sentences,and/or others, and to request feedback as to the original author, latestversion, original publication date and/or historically relatedreferences. A system that determines a publication date and/or author ofan object or document in one embodiment may include a historicalanalytics engine 104, a stream analytics engine 106, a contextualanalytics and parsing engine 108, and a media analysis engine 110 toanalyze other media sources. The components (e.g., 102, 104, 106, 108,110) may be computer-implemented modules that run or execute on one ormore hardware processors, which may be located on a single physicalmachine or distributed across multiple physical machines.

The user interface 102 may allow a user to specify preferences such aswhich topics or document types should be analyzed and what informationshould be included in the report. In one aspect, a user may specifythese preferences in a profile and/or a user may request analysis andprovide preferences at the initiation of the search and/or after asearch result has been returned. For instance, the user interface 102may allow a user to specify a profile that may be stored and retrievedto determine user preferences. In another aspect, the user interface 102may allow a user to create a profile that may be stored and retrieved.Yet in another aspect, the user interface 102 may provide a form withone or more fields on a graphical user interface window that can bepopulated or filled in with the user preferences. The information theuser populates may be stored as a profile. The user may also be allowedto select or input information sources to search, for example, theInternet, an organization's database, and/or other sources.

In one embodiment, a user entered search string may be received via theuser interface 102. The user interface 102, for example, may be a userinterface associated with a search engine, and for example, may be oneor more of an Internet search engine browser, any other software orhardware program or function with browsing capability for data or Webbrowsing capability. A search engine may be invoked that performs, e.g.,an Internet search (or another data network or source search) based onthe user entered search string. A search engine, e.g., returns a list ofreferences or documents determined to be relevant to the search. Ahistorical analytics engine 104, for example, using a contextualanalytics and parsing engine 108 may parse the user search string andfind or search for references (e.g., all references) that contain thesearch string or a phrase in the search string in the title of thereference or document. The references may be references or documentsfound in the World Wide Web (Web) via an Internet search performed by asearch engine or documents found from any other data sources, forexample, an organization's database or another. In one aspect, thehistorical analytics engine 104, stream analytics engine 106, mediaanalysis engine 110 and contextual analytics and parsing engine 108 maybe components of a search engine that performs a search based on theuser entered search string and returns a list of results. In anotheraspect, the components 104, 106, 108, 110 may be separate programs thatmay be invoked, e.g., via application programming interfaces. Yet inanother aspect, the functionalities of the components 104, 106, 108, 110may be implemented in a single program. Any other implementationmechanism may be utilized to implement the functionalities of thosecomponents described herein.

If a document with the exact title is found and the author andpublication date are found, the processing ends. For example, the usercan specify whether the input (in the search string) is a title andshould be matched exactly. Otherwise discovering the title of a documentwith the target search string is a function of the analysis. Forexample, analysis is performed that may identify the title of thedocument based on the target search string.

If the search string is designated as an exact title and none is found,the user can continue the search with other forms of input. For example,if no title is found that contains the search string or a phrase in thesearch string, the historical analytics engine 104, for example, usingthe contextual analytics and parsing engine 108, may search forreferences containing the search string in the text of those references.Natural language processing may be used in this search. If the searchstring, author and publication date are found in the text of one or moreof the references, the processing ends.

In one embodiment, a methodology and/or system of the present disclosuremay use the least expensive function first and move to progressivelymore complicated analytics, for example, to optimize computationalperformance and use of resources in a computer system. The historicalanalytics engine 104 may check for titles matching the input title, ifone is given. Then the historical analytics engine 104 may attempt tofind the search string in the body of text documents matching the typesselected by the user such as publications, articles, news stories, andor others. This search may include a basic text matching search. Next,the historical analysis engine 104 may broaden the search by usingcontextual analysis, writing styles, language translation and/or idioms,and other natural language processing techniques. For instance, thehistorical analysis engine 104 may check first whether the input is atitle, and if so, try to match the user title string (search string)with the title of a ‘document’. The historical analytics engine 104returns a result to the user and if null, the user can elect to continuethe search. The historical analysis engine 104 may be invoked again(possibly with additional input), for example, responsive to the userelecting to continue the search, and the historical analysis engine 104searches for the string (which may be the original title or a modifiedstring) in the body of a document. The historical analysis engine 104also can exploit contextual search, writing style matching, idioms, andlanguage translation to find documents with wording that has the same orsimilar meaning or connotation as the input. If this fails, streamanalysis may be invoked as described below.

If the search string is not found in the document title (title of areference) and the search string is not found in the text of thedocument (reference) with author and publication date, a streamanalytics engine 106 may be invoked. For example, if the historicalanalytics engine 104 does not provide the publication information, astream analytics engine 106 may be invoked. The stream analytics engine106 looks for or searches to find the search string in the body of abroader range of sources like social media posts, voice documents, videodocuments, recorded media, possibly phone conversations and/or textmessages. This search may include a contextual search using idioms,voice translation, language translation, and/or other, for example,using the contextual analytics and parsing engine 108.

If the stream analytics engine 106 does not provide the publicationinformation, further analytics may be performed by a media analysisengine 110. Thus, a methodology and/or system in one embodiment of thepresent disclosure may include a multiple hierarchy of searches thatlooks for additional information such as author's profession, education,experience and other information.

A media analysis (or analytics) engine 110 searches to find additionalreference material to verify and/or correlate conflicting or incompleteconclusions from the historical and stream analytics functions. Forexample, the media analytics engine 110 may search for indirectlyrelated persons and information sources. The media analytics engine 110may use artificial intelligence to establish probable relationshipsbetween all of the above collected search results and formulate aprobability of the accuracy of the most likely outcome. Functions of themedia analytics engine 110 broaden the search to find background datarelative to authors and dates that may resolve inconsistencies, forexample, when there are multiple possible authors and dates.

The above described analytics that comprise a hierarchical drill downmethodology in one embodiment is shown with reference to FIG. 2.Resulting references from performing the drill down analytics may beresolved. The search of the references may end and the results (obtainedpublication information) may be presented or displayed to the user, forexample, via the user interface 102, for example, according to the userpreference.

FIG. 2 is a flow diagram illustrating an overview of a drill downanalytics method for determining or estimating article or objectpublication dates in one embodiment of the present disclosure. At 202, ahistorical drill down may include performing a historical analysis byretrieving text of all publications, news stories, articles, and othersuch objects on the topic or subject matter specified by the user in thesearch string. Author and dates where available may be also retrieved.At 204, if no documents are found from performing the historicalanalysis, the logic of the method proceeds to 206 to stream drill down.If documents are found at 204, at 218, textual analysis of subjectmatter, writing style, wording, spelling, idioms and other naturallanguage processing may be performed to find the author and date (andoptionally other information) about the publication (specified in theuser entered search string) in the text of the documents. At 220, if theauthor and date information about the publication is found in thetextual analysis, the logic of the method proceeds to 214. At 220, ifthe author and date information is not found from the analysis, at 222,it is determined whether there is any confirming or opposing commentsfrom other authors discovered in the textual analysis. If there are noconfirming or opposing comments about the publication from otherauthors, the logic of the method proceeds to 206 to stream drill down.If at 222 it is determined that the textual analysis uncovered aconfirming or opposing comment about the publication from one or moreother authors, the logic of the method proceeds to 214 to resolve theinformation so far discovered including the confirming or opposingcomment.

The historical analysis, for example, may take as input one or more ofthe following parameters: a document title (e.g., article, technicalpaper, software program name) or search string text (e.g., to be foundembedded in a document). Optionally, the historical analysis may alsotake as input a version number, document type(s) to search. Thehistorical analysis may output an author and publication date, and ifthere is an indication of a version of the document, the most recentversion number, for example. If the above output cannot be determinedduring the first pass, control to user may be returned to a user. Theuser may decide to continue or discontinue the search. For example, thehistorical analysis may include receiving input from the user as towhether to continue the search. If no, then the logic of the processends. If yes, that is, the user selected to continue the search, thehistorical analysis may further use contextual analysis, writing styles,language translation, idioms, and/or other analysis to find author andpublication date or latest version number. In one embodiment, theadditional analysis to continue the search may be performedautomatically without returning control to the user and receiving userinput. In one embodiment, a user may specify in a profile the option toautomatically continue or discontinue the search without returningcontrol to the user. The historical analysis outputs author andpublication date, and may also output the latest version number, iffound. If not found, stream analytics engine may be called.

At 206, stream drill down may be performed. The processing at 206 mayinclude a stream analysis, for example, which may include searching datastreams for text similar to or matching the search string (and/orassociated topic) and retrieving text of all blogs, posts, microblogs,social network and social media references to this topic, retrievingauthors and dates where available using contextual searches, andtranslating voice or other media to text. These references retrieved bystream analysis are referred to as secondary references. The streamanalysis may also include searching social media sites for references tothe uniform resource locator (URL) of the document being searched. Thismay include recursively expanding shortened URLs that are common on somesites. By using the earliest reference to a particular document onsocial media, the date of publication may be narrowed down and may helprefine a result in combination with other analysis performed. At 208, ifone or more references associated with the topic or publication in thestream analysis is found but the date associated with the one or morereferences is not available, the logic of the method proceeds to 210 tosearch other sources. If no references are found that are associatedwith the user entered search string (publication or topic) by the streamanalysis, the logic of the method proceeds to 214. In case wherereferences are found by the stream analysis including publication dateand/or author information, the logic of the method may also proceed to214.

Stream analysis may take as input all user inputs from the historicalengine or historical analysis (e.g., titles, strings, document types,version numbers) and all outputs from the historical analysis (e.g.,references, probable dates, probable versions, probable authors). Streamanalysis may output additional information or data such as references,probable dates, probable versions, and/or probable authors.

At 210, media analysis is performed. Media analysis at 210 may includesearching other sources, for example, other documents related to one ormore authors found in stream analysis at 206, social network comments,for example, by friends of the one or more authors found in streamanalysis at 206, travel information, school yearbooks, classmates,acquaintances or authors, and posts by relatives and friends of authors.The references discovered by the media analysis at 210 are referred toas tertiary references. If relatives or classmates of one or moreauthors are found in the media analysis, documents pertaining to therelatives or classmates and the dates associated with those documentsare searched. If no additional references are found in the mediaanalysis, the logic of the method proceeds to 214.

Media Analysis may take as input all previous inputs and outputs fromhistorical analysis and stream analysis. The media analysis or a mediaanalytics engine searches to find additional reference material toverify and/or correlate conflicting or incomplete conclusions from thehistorical and stream analytics functions. The media analysis, forexample, may include searching for indirectly related persons andinformation sources. The media analysis may use artificial intelligenceto establish probable relationships between all of the above collectedsearch results and formulate a probability of the accuracy of the mostlikely outcome. The media analytics engine or the media analysis, in oneaspect, broadens the search to find background data relative to authorsand dates that may resolve inconsistencies when there appears to bemultiple possible authors and dates. The media analytics engine or themedia analysis outputs a report (e.g., a final report), which mayinclude a list of references found from previous steps. For eachreference, the media analytics engine or the media analysis outputspossible authors and dates each with a probability of accuracy.

At 214, the media analysis may also include resolving the referencesdiscovered in the above-described analysis processes (e.g., historicalanalysis, stream analysis, and/or media analysis). Resolving referencesat 214 in one embodiment includes creating a time line with authors,acquaintances, secondary references and dates, tertiary references anddates. A probability value may be assigned that indicates whether eachdate is related to the original search. The original search, forexample, is the title or text string input by the user. During thesearching operations, it may be true that the user input a title thatwas inaccurate, but based on the matches found by the various engines(or analysis processes) there may be a high probability that a somewhatdifferent title is really the one the user intended. In one embodiment,the probabilities may be computed based on conclusions of the mediaanalysis or engine, for example, using artificial intelligence, as towhether the relationships between the document, its content, the authorsand the dates are consistent with each other and consistent with whatthe user seems to be searching for. Dates in the timeline with highestprobability of having a relationship to the search may be selected. Mostprobable date range may be selected for determining the publication dateof an article or object. An overall confidence level may be assigned tothe selected dates.

At 216, a report may be generated including publication information, forexample, the determined date and/or authors. The content and format ofthe report may be tailored based on the user preference, for example,specified by a user in a profile and/or via the user interface. Thereport may be presented on a user interface.

The following illustrates an example of a time line analysis. Consideras an example that the search string includes “Aborigine TribalPractices—exocannibals consume rivals”. The following information isdiscovered via the historical analytics, stream analytics and/or mediaanalytics:

1. Author M blog Feb. 12, 2007 several paragraphs on Aborigine tribalcustoms, used words exocannibal and rival.2. Search on Author M shows low probability of being the originalauthor—she is a county clerk.3. Search on Author M shows student at University of M from 2004-2008.4. Author N's social network post on May 6, 2011 contained word“exocannibal”.5. Search on Author N shows low probability of being the originalauthor—he is a novelist.6. Search on Author N in a social network site shows that he waspreviously an archaeology professor from 2004 to 2008 and the socialnetwork site has numerous photos and posts about his first novel, “HonorAmong Head Hunters”.7. Search of “Honor Among Head Hunters” on a web site has a photo ofauthor next to a tribal native in a tribal village with tents inbackground.8. Facial recognition technique shows the author is Author N.9. A crowd sourced information website with no publication date usedword “exocannibals” but bibliography refers to 2013 encyclopedia as asource.10. An online encyclopedia article shows date that is not early enough.

The following probabilities are assigned based on the above findings:

1. Author M as author has low probability, e.g., 10%.2. Author N as author has high probability, e.g., 90%.3. Date later than 2003 has high probability, e.g., 100%.4. Date earlier than 2013 has high probability, e.g., 99%.5. Date earlier than Feb. 12, 2007 has high probability, e.g., 90%.6. Most probable date 2006.

Based on the above timeline with assigned probability on different datesof publication, a user report is generated. The user report may identifythat the probable author of original material used in Author M's blog isAuthor N, with probable origination date between 2004 and 2007, e.g.,the probability that the authors and publication dates are correct.

FIG. 4 is another diagram illustrating components that may estimateinitial publication information of an object (e.g., article or document)based on social media context in one embodiment of the presentdisclosure. As described with reference to FIGS. 1 and 2, analyticsengines 302 (e.g., historical, stream, and media analytics engines) maysearch one or more data sources 306, 308, 310, 312, 316 to discover andresolve publication information (e.g., author and publication date)associated with an object such as an article or document. Also asdescribed above, the data sources 306, 308, 310, 312, 316 may includedata from the Internet, social media data, organization's databaseand/or other sources. One or more of the analytics engines 302 mayconnect to, search for, and retrieve information, from one or more ofthe data sources 306, 308, 310, 312 via a network 318. One or more ofthe analytics engines 302 may connect to, search for, and retrieveinformation, from one or more of the data sources (e.g., 316) locally.For example, a data source may be connected locally to one or moreprocessors running the one or more analytics engines 302. A userinterface 304 may run on one or more processors that may be locallyconnected to the one or more processors running the one or moreanalytics engines 302, or remotely connected to the one or moreprocessors running the one or more analytics engines 302 via a network318.

A technique of the present disclosure, for example, as described above,estimates a document's initial publication information, for example,based on social media context analysis. The technique may includeapplying natural language processing (NLP) to a document to identifyattributes of the document, and searching social media and otherrepositories of information, for example, the Internet and/or other datasources, recursively for information comprising references, dates, andauthors matching at least one attribute of the document to determinecorrelations between portions of information and to determine initialpublication information. The initial publication information of thedocument is presented or displayed on a user interface, for example, toa user. In one aspect, the initial publication information may compriseinformation such as date, data range, author, title, and location. Acalculated probability of correctness may be displayed. In one aspect,the repositories of information may include environmental data, socialmedia data, blogs, microblogs, posts, historical information. Analyticssuch as textual analysis, statistical analytics, and artificialintelligence (AI) may be performed to determine the initial publicationinformation.

FIG. 4 illustrates a schematic of an example computer or processingsystem that may implement the publication date estimation system in oneembodiment of the present disclosure. The computer system is only oneexample of a suitable processing system and is not intended to suggestany limitation as to the scope of use or functionality of embodiments ofthe methodology described herein. The processing system shown may beoperational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with the processing system shown in FIG. 4 may include,but are not limited to, personal computer systems, server computersystems, thin clients, thick clients, handheld or laptop devices,multiprocessor systems, microprocessor-based systems, set top boxes,programmable consumer electronics, network PCs, minicomputer systems,mainframe computer systems, and distributed cloud computing environmentsthat include any of the above systems or devices, and the like.

The computer system may be described in the general context of computersystem executable instructions, such as program modules, being executedby a computer system. Generally, program modules may include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types.The computer system may be practiced in distributed cloud computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed cloudcomputing environment, program modules may be located in both local andremote computer system storage media including memory storage devices.

The components of computer system may include, but are not limited to,one or more processors or processing units 12, a system memory 16, and abus 14 that couples various system components including system memory 16to processor 12. The processor 12 may include a module 10 that performsthe methods described herein. The module 10 may be programmed into theintegrated circuits of the processor 12, or loaded from memory 16,storage device 18, or network 24 or combinations thereof.

Bus 14 may represent one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

Computer system may include a variety of computer system readable media.Such media may be any available media that is accessible by computersystem, and it may include both volatile and non-volatile media,removable and non-removable media.

System memory 16 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) and/or cachememory or others. Computer system may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 18 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(e.g., a “hard drive”). Although not shown, a magnetic disk drive forreading from and writing to a removable, non-volatile magnetic disk(e.g., a “floppy disk”), and an optical disk drive for reading from orwriting to a removable, non-volatile optical disk such as a CD-ROM,DVD-ROM or other optical media can be provided. In such instances, eachcan be connected to bus 14 by one or more data media interfaces.

Computer system may also communicate with one or more external devices26 such as a keyboard, a pointing device, a display 28, etc.; one ormore devices that enable a user to interact with computer system; and/orany devices (e.g., network card, modem, etc.) that enable computersystem to communicate with one or more other computing devices. Suchcommunication can occur via Input/Output (I/O) interfaces 20.

Still yet, computer system can communicate with one or more networks 24such as a local area network (LAN), a general wide area network (WAN),and/or a public network (e.g., the Internet) via network adapter 22. Asdepicted, network adapter 22 communicates with the other components ofcomputer system via bus 14. It should be understood that although notshown, other hardware and/or software components could be used inconjunction with computer system. Examples include, but are not limitedto: microcode, device drivers, redundant processing units, external diskdrive arrays, RAID systems, tape drives, and data archival storagesystems, etc.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements, if any, in the claims below areintended to include any structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description of the present invention has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the invention in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The embodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

1.-8. (canceled)
 9. A computer readable storage medium storing a programof instructions executable by a machine to perform a method ofestimating initial publication information based on social media contextanalysis, the method comprising: receiving via a user interface, asearch string associated with an object; searching for one or moredocuments having a title that matches the search string and publicationinformation associated with the one or more documents, the publicationinformation comprising at least one or more authors and publicationdates; searching for the one or more documents that contain the searchstring within the content of the one or more documents and thepublication information associated with the one or more documents usinga natural language processing technique, responsive to not finding theone or more documents having the title that matches the search string;searching social media data for information associated with the searchstring, topic associated with the search string, and the one or moredocuments and the publication information; and resolving the one or moredocuments and the publication information associated with the one ormore documents and the information found in the social media data usingan artificial intelligence technique; generating a report comprising thepublication information; and presenting the report on the userinterface.
 10. The computer readable storage medium of claim 9, whereinthe report comprises an assigned probability associated with each of theone or more authors and publication dates.
 11. The computer readablestorage medium of claim 10, wherein the publication dates are listed asranges of dates in the report.
 12. The computer readable storage mediumof claim 9, wherein the social media data comprises one or more ofblogs, microblogs, social network posts, social media posts.
 13. Thecomputer readable storage medium of claim 9, wherein the searching thesocial media data further comprises searching for one or more uniformresource locator associated with the one or more documents and theobject.
 14. The computer readable storage medium of claim 9, wherein theresolving the one or more documents and the publication informationassociated with the one or more documents and the information found inthe social media data further comprises generating a timeline using theone or more documents and the publication information associated withthe one or more documents and the information found in the social mediadata.
 15. The computer readable storage medium of claim 9, wherein thepublication information further comprises one or more of the title andlocation.
 16. The computer readable storage medium of claim 9, whereinthe searching for the one or more documents that contain the searchstring within the content of the one or more documents and thepublication information associated with the one or more documentscomprises searching one or more of Internet data, historical data, andenvironmental data.
 17. A system for estimating initial publicationinformation based on social media context analysis, comprising: a userinterface operable to run on one or more processors, the user interfaceoperable to receive a search string associated with an object; ahistorical analytics engine operable to run on the one or moreprocessors, the historical engine further operable to search for one ormore documents having a title that matches the search string andpublication information associated with the one or more documents, thepublication information comprising at least one or more authors andpublication dates, the historical analytics engine further operable tosearch for the one or more documents that contain the search stringwithin the content of the one or more documents and the publicationinformation associated with the one or more documents using a naturallanguage processing technique; a stream analytics engine operable to runon the one or more processors, the stream analytics engine furtheroperable to search social media data for information associated with thesearch string, topic associated with the search string, and the one ormore documents and the publication information; and a media analyticsengine operable to run on the one or more processors, the mediaanalytics engine further operable to resolve the one or more documentsand the publication information associated with the one or moredocuments and the information found in the social media data using anartificial intelligence technique, the media analytics engine furtheroperable to generate a report comprising the publication information andpresent the report on the user interface.
 18. The system of claim 17,wherein the report comprises an assigned probability associated witheach of the one or more authors and publication dates.
 19. The system ofclaim 18, wherein the publication dates are listed as ranges of dates inthe report.
 20. The system of claim 1, wherein the social media datacomprises one or more of blogs, microblogs, social network posts, socialmedia posts.